Search Results (994)

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. Full article

Soilless vegetable production in the Atacama Desert of Northern Chile is spreading since it is perceived as an alternative that requires much less water than open field soil production. However, strong competition between mining and urban use for human population consumption exists, forcing growers to use alternative water sources. Fog is commonly present in the coastal areas of Northern Chile; however, little information exists with regards to its chemical composition and the effect on nutrient quality of the produce. To address this knowledge gap, a set of experiments was carried out in Chañaral, a small town located in the Atacama Desert of Northern Chile. There, a 200 m² greenhouse equipped with twenty deep flow pools was used in two consecutive growing cycles. Water for the mixing of the nutrient solution was collected from the fog using fog-catchers and later stored in 2000-L tanks. Fog water quality (electrical conductivity, pH and mineral content) was monitored directly from the storage tanks. Two types of lettuce, green butterhead and red oak leaf, were compared on their yield and accumulation of nutrients and heavy metals. The results indicate that fog water is of good quality for soilless production, with an electrical conductivity value of 0.65 ± 0.18 and low content of heavy metals. Plants’ heavy metal accumulation is below the recommendation of Food and Agriculture Organization and World Health Organization. Fog water presents as a viable water source for soilless production in Northern Chile. Full article

The global community is grappling with a significant increase in forest fires’ frequency, size, and intensity, presenting a profound challenge. To complement existing framing literature on forest fires, this paper examines collective frames applied to forest fires in a broader EU context. Employing a content analysis covering 354 EU policy documents—spanning both soft (non-legally binding) and hard (legally binding) policy documents—via the use of Atlas.ti, six collective frames on forest fires are outlined, identifying four as particularly dominant: ‘climate adaptation and resilience’, ‘risk mitigation and protective governance’, ‘agriculture and rural development’, and ‘access to information on forest fires’. These frames capture dominant perspectives promoted within specific policy domains, such as energy and agriculture. Despite the diverse approaches to framing forest fires and their varied objectives, a common thread connects the narratives in these documents, namely, the central theme of ‘risk’. Whether it emerges in the context of reporting or as part of a call to action for adopting certain EU measures, the use of risk operates as a narrative device that negatively frames the discourse, consistently employed to call for action. The findings underscore the importance of considering communication strategies surrounding forest fires, particularly in light of their implications for forest governance. Full article

Phenacoccus solenopsis Tinsley is a pest that poses a significant threat to agricultural crops, especially cotton, and is now widely distributed across many regions worldwide. In this study, we performed a meta-analysis on the collected experimental data and found that within the suitable temperature range, the survival rate of P. solenopsis increases with rising temperatures, indicating that climate plays a decisive role in its distribution. Using the MaxEnt model this study predicted that under three future climate scenarios (SSP1–2.6, SSP3–7.0, and SSP5–8.5), the distribution of P. solenopsis will expand and move towards higher latitudes. Climate change is the primary factor influencing changes in pest distribution. We conducted a meta-analysis of P. solenopsis, including seven independent studies covering 221 observation results, and examined the impact of temperature ranging from 18 °C to 39 °C on the developmental cycle of P. solenopsis. As the temperature rises, the development cycle of P. solenopsis gradually decreases. Additionally, by combining the MaxEnt model, we predicted the current and potential future distribution range of P. solenopsis. The results show that under future climate warming, the distribution area of P. solenopsis in China will expand. This research provides a theoretical basis for early monitoring and control of this pest’s occurrence and spread. Therefore, the predictive results of this study will provide important information for managers in monitoring P. solenopsis and help them formulate relevant control strategies. Full article

Traditional deep learning methods employing 2D images can only classify healthy and unhealthy seedlings; consequently, this study proposes a method by which to further classify healthy seedlings into primary seedlings and secondary seedlings and finally to differentiate three classes of seedling through a 3D point cloud for the detection of useful eggplant seedling transplants. Initially, RGB images of three types of substrate-cultivated eggplant seedlings (primary, secondary, and unhealthy) were collected, and healthy and unhealthy seedlings were classified using ResNet50, VGG16, and MobilNetV2. Subsequently, a 3D point cloud was generated for the three seedling types, and a series of filtering processes (fast Euclidean clustering, point cloud filtering, and voxel filtering) were employed to remove noise. Parameters (number of leaves, plant height, and stem diameter) extracted from the point cloud were found to be highly correlated with the manually measured values. The box plot shows that the primary and secondary seedlings were clearly differentiated for the extracted parameters. The point clouds of the three seedling types were ultimately classified directly using the 3D classification models PointNet++, dynamic graph convolutional neural network (DGCNN), and PointConv, in addition to the point cloud complementary operation for plants with missing leaves. The PointConv model demonstrated the best performance, with an average accuracy, precision, and recall of 95.83, 95.83, and 95.88%, respectively, and a model loss of 0.01. This method employs spatial feature information to analyse different seedling categories more effectively than two-dimensional (2D) image classification and three-dimensional (3D) feature extraction methods. However, there is a paucity of studies applying 3D classification methods to predict useful eggplant seedling transplants. Consequently, this method has the potential to identify different eggplant seedling types with high accuracy. Furthermore, it enables the quality inspection of seedlings during agricultural production. Full article

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system’s performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management. Full article

Exploring the phenotypic trait variation and diversity of Lycium ruthenicum germplasm resources can support selection, breeding, and genetic improvement, enhancing agricultural production. This study collected 213 wild Lycium ruthenicum seedlings from a resource nursery in Alxa League, Inner Mongolia. These seedlings originated from eight sources across four provinces. Using 11 pseudo-qualitative traits and 20 quantitative traits, the phenotypic variation of the germplasm was analyzed. The analysis involved the coefficient of variation, Shannon–Wiener index (H), Simpson’s genetic diversity index (D), principal component analysis, correlation analysis, and Q-type cluster analysis. The results showed that the variation range of 31 phenotypic traits across the 213 Lycium ruthenicum germplasm resources was 17.26% to 105.41%, with an average coefficient of variation of 39.85%. The H and D indexes ranged from 0.18 to 1.58 and 0.20 to 0.75, respectively. For the 11 pseudo-qualitative traits, the H and D ranges were 0.18 to 1.58 and 0.07 to 0.74, with average values of 0.77 and 0.42. For the quantitative traits, the H and D ranges were 0.54 to 1.49 and 0.25 to 0.75, with average values of 1.21 and 0.63. This indicates that Lycium ruthenicum germplasm resources exhibit significant phenotypic diversity, with quantitative traits showing higher diversity than pseudo-qualitative traits. Principal component analysis revealed that the cumulative variance contribution rate of the first 10 principal components was 74.03%, comprehensively reflecting the information of the 31 traits. Q-type cluster analysis grouped the 213 Lycium ruthenicum germplasm resources into six clusters, each with distinct phenotypic characteristics. This analysis also identified the trait characteristics and breeding value of each cluster. The results of this study provide valuable information on the genetic improvement, conservation, and evaluation of Lycium ruthenicum germplasm resources. Full article

Background/Objectives: Women of childbearing age not only reside in agricultural communities but also form an integral part of the agricultural labor force. Limited research investigates the impact of prenatal fungicide exposure on infant health, specifically ethylenebisdithiocarbamates and their toxic by-product, ethylenethiourea (ETU), particularly in occupational settings. This paper describes the background, aims, protocol, and baseline sample characteristics for the SEMILLA study, which investigates prenatal ETU exposure, neonatal thyroid function, infant growth, and neurobehavioral development in an agricultural region of Ecuador. Methods: This cohort study follows pregnant women and their infants up to 18 months of age, incorporating urinary biomarkers and survey data on ETU exposure and infant growth and neurodevelopmental measures. Data collection includes detailed questionnaires, scales, and physical examinations on maternal and infant health and development, as well as environmental factors. Descriptive statistics on key characteristics of the study population at baseline are presented. Results: SEMILLA enrolled 409 participants (72% enrollment rate): 111 agricultural workers (mostly floricultural), 149 non-agricultural workers, and 149 non-workers. Baseline characteristics show comparability between work sector groups, with some economic differences. Conclusions: SEMILLA will provide key evidence on prenatal fungicide exposure and infant development and encompass comprehensive multistage data collection procedures in pregnancy and infancy, focusing on structural and social determinants of health as well as individual-level chemical exposures. The community-based approach has proven essential, even amid challenges like the COVID-19 pandemic. The medium-term objective is to inform sustainable interventions promoting maternal and child health, with a long-term goal to reduce community exposures and improve worker health policies, particularly for women and pregnant workers. Full article

Evidence-based research on the effects of rainfall, temperature, and relative humidity variability on maize yield is essential for understanding the climate dynamics of, and paving the way for informed adaptive solutions to future potential negative impacts in, Dschang-Cameroon. This study employed the non-parametric Mann–Kendall and Sen’s slope method to detect trends in climate variables and maize yield in the period between 1990 to 2018. Pearson correlation and multilinear regression (MLR) analyses were also used to establish the linear relationship between climate variables and maize yield, and to explore the behavior of the response variable (maize yield) with the predictor variables (climatic variables), respectively. In addition, perceptions of climate variability and its impact on maize yield from a hundred farmers were collected through a questionnaire and analyzed in SPSS. Twenty key informants’ interviews (KII) were conducted using a semi-structured interview and analyzed by thematic analysis. The results showed that the minimum temperature exhibited a decreasing trend at a rate of 0.039 °C per annum, whereas relative humidity had an increasing trend of 0.25% per annum with statistical significance at p = 0.001. In addition, a decreasing trend of rainfall, at a rate of 4.94 mm per annum, was observed; however, this had no statistical significance. Furthermore, the MLR analysis showed that mean temperature and relative humidity have an inversely proportional but statistically significant relationship with maize yield (p = 0.046 and p = 0.001, respectively). The analysis of farmers’ perceptions confirmed the results of trend analyses of decreasing rainfall and increasing maximum temperatures. Moreover, the farmers asserted that the vulnerability of farmers to climate variability is also linked to gender and locality, where women’s outputs are more assailable and farms in low-lying areas are more prone to floods. The high price of farm inputs was also reported as a key factor, other than climate variability, hindering the flourishing of the maize sector in Dschang. Finally, an analysis of the KII indicated the inadequate implementation of flagship agricultural programs in the locality. Full article

Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers’ livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword “phytoplasma” and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections. Full article

This research investigated the role of agricultural cooperatives (ACs) in promoting sustainable agriculture and agrifood security, with a particular emphasis on Greece. A cross-sectional survey technique was employed, and data were collected from 400 farmers and professionals either employed by or associated with Greek agricultural cooperatives by administering an online questionnaire. A response rate of 96.5% was achieved. The study findings show that cooperative techniques bring about a positive shift in agrifood security and sustainable agriculture. Particularly, participants concurred that resource sharing among cooperative members increases farm productiveness and sustainability by 94.2% while improving access to credit and financial support by 91.5%. Moreover, 84.3% agreed that access to up-to-date information enhances the practice of sustainable agriculture, and 95.1% agreed that collective bargaining through cooperatives increases the prices of agricultural commodities. Regarding the application of advanced technologies in cooperative practices, 96.7% of the participants acknowledged that it improved farm efficiency. The cooperative model demonstrates how agricultural expansion may be achieved by collective bargaining, information sharing, resource sharing, and technological integration, while also considerably improving agrifood security and sustainability. These findings highlight the crucial importance of cooperatives in increasing the level of agricultural production, ensuring sustainability, and improving agrifood security in Greece. Full article

Jamun plant displays enormous diversity throughout Pakistan, which necessitates its screening, evaluation, and validation to document elite genotypes having better traits for the benefit of the fruit industry and farmers. Surveys were made in natural Jamun habitats across Punjab, Pakistan, and genotypes were marked based on visual diversity of trees and fruits. In total, 60 Jamun genotypes were selected for characterization based on phenotypic and genetic markers. Phenotypic characters related to trees, leaf, and flower along with fruit qualitative traits were assessed in situ. Results revealed significant diversity with high (>25%) coefficient of variance values and the first two components of correspondence analysis exhibited 41.71% variation among genotypes. A strong association was observed among traits like upright tree and round fruit shape (0.74), bluish-colored fruit and pinkish pulp (0.85), and elliptic-shaped fruit with low fruit waxiness (−0.72). Leaves of phenotypically characterized plants were brought to Wheat Biotechnology Lab., University of Agriculture, Faisalabad, Pakistan, where Jamun genotypes were investigated genetically using Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR) markers. A total of 132 bands were scored, of which 108 were polymorphic, corresponding to almost 81% polymorphism among collected genotypes. High polymorphism information content values were observed against RAPD (0.389) and ISSR (0.457) markers. Genotypes were compared in relation to genetic markers, which exhibited that almost 86% of genetic variability was attributed to differences among accessions, while 14% of variation was due to differences between collections of different areas. Findings of this study confirmed wide phenotypic and genetic distinctness of Jamun in Pakistan that can aid breeders for marker-assisted selection and germplasm enhancement for future crop improvement programs. Full article

This article presents a detailed analysis of a unique item from the author’s family archive: the work diary of his grandfather, Mordechai Livnat (Libman). In this diary, Livnat meticulously recorded, between 1928 and 1931, the details of his work as an agricultural laborer in Herzliya—at the time, a small village in the central part of Eretz Israel (aka pre-State Israel)—primarily during the establishment of the new colony’s citrus orchards. The diary documents employment details, employer information, working hours, and wages received. Quantitative and qualitative analyses of the information contained in the diary paint a comprehensive picture that allows us to learn about the lives of Jewish agricultural laborers in Eretz Israel at that time. In particular, the hardships faced by these workers stand out, primarily job insecurity, which manifested mainly in their dependence on the weather and the need to work for multiple employers. This article also sheds light on aspects related to agricultural work before the introduction of technological advancements to the agricultural sector, which was mainly manual then, and its impact on the daily routine of the agricultural laborer. The diary is analyzed using an inductive approach—from the text outwards—in a way that emphasizes the complexity and importance of the connections between the macro and micro in historical research. This way, it is demonstrated how items collected during genealogy research can shed important light on historical knowledge, and not just the other way around. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras offer great potential for applications in precision agriculture. A critical challenge that limits the deployment of this technology is the varying ambient illumination caused by cloud movement. Rapidly changing solar irradiance primarily affects the radiometric calibration process, resulting in reflectance distortion and heterogeneity in the final generated orthomosaic. In this study, we optimized the radiometric block adjustment (RBA) method, which corrects for changing illumination by comparing adjacent images and from incidental observations of reference panels to produce accurate and uniform reflectance orthomosaics regardless of variable illumination. The radiometric accuracy and uniformity of the generated orthomosaic could be enhanced by improving the weights of the information from the reference panels and by reducing the number of tie points between adjacent images. Furthermore, especially for crop monitoring, we proposed the RBA-Plant method, which extracts tie points solely from vegetation areas, to further improve the accuracy and homogeneity of the orthomosaic for the vegetation areas. To validate the effectiveness of the optimization techniques and the proposed RBA-Plant method, visual and quantitative assessments were conducted on a UAV-image dataset collected under fluctuating solar irradiance conditions. The results demonstrated that the optimized RBA and RBA-Plant methods outperformed the current empirical line method (ELM) and sensor-corrected approaches, showing significant improvements in both radiometric accuracy and homogeneity. Specifically, the average root mean square error (RMSE) decreased from 0.084 acquired by the ELM to 0.047, and the average coefficient of variation (CV) decreased from 24% (ELM) to 10.6%. Furthermore, the orthomosaic generated by the RBA-Plant method achieved the lowest RMSE and CV values, 0.039 and 6.8%, respectively, indicating the highest accuracy and best uniformity. In summary, although UAVs typically incorporate lighting sensors for illumination correction, this research offers different methods for improving uniformity and obtaining more accurate reflectance values from orthomosaics. Full article

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum’s Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a “high-high” and “low-low” (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country’s overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region. Full article